Extracting affine-invariant regions and features from image data is used in numerous computer vision and robotic applications (e.g., image recognition and retrieval, mosaicing, three dimensional (3D) reconstruction, robot navigation, etc.) Such features have been tracked using corner detectors also used for stereo-matching and 3D vision-guided navigation. Affine-invariant feature detectors have become more sophisticated, both in terms of their invariance to scale changes and rotation and in terms of the complexity of supported applications image matching and retrieval instead of simple stereovision. Affine-invariant feature detectors have also been proposed to accurately handle the problem of perspective distortions.
An important aspect in feature detection is the ability of the detector to reliably find the same feature under different viewing conditions. One fairly reliable feature detection approach is the Maximally Stable Extremal Regions (MSER) approach proposed by Matas et al. MSER has become an industry standard due to its ability to find the same feature under different viewing conditions. Due to the relatively small number of regions per image, MSER is complementary to many common detectors and is well suited for large scale image retrieval. MSER has been used in a wide spectrum of computer vision applications (e.g., wide-baseline stereo, object recognition, image retrieval, tracking in temporal domain in consecutive frames, and 3D segmentation).
MSER has been shown to perform well as compared to other local detectors. MSER, however, is highly dependent on the intensity of incoming images. Accordingly, improved approaches and systems for reliably and efficiently identifying features in image data for use in object detection and/or object tracking remain of interest.